In [1]:
import pandas as pd
import numpy as np
import plotly.express as px
import matplotlib.pyplot as plt
print('modlues are imported')

modlues are imported

Loading the dataset¶

In [2]:
dataset_url = 'https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv'
df = pd.read_csv(dataset_url)

Understanding the data¶

Loading the dataset¶

In [3]:
df.head()
Out[3]:
Date Country Confirmed Recovered Deaths
0 2020-01-22 Afghanistan 0 0 0
1 2020-01-23 Afghanistan 0 0 0
2 2020-01-24 Afghanistan 0 0 0
3 2020-01-25 Afghanistan 0 0 0
4 2020-01-26 Afghanistan 0 0 0
In [4]:
df.tail()
Out[4]:
Date Country Confirmed Recovered Deaths
161563 2022-04-12 Zimbabwe 247094 0 5460
161564 2022-04-13 Zimbabwe 247160 0 5460
161565 2022-04-14 Zimbabwe 247208 0 5462
161566 2022-04-15 Zimbabwe 247237 0 5462
161567 2022-04-16 Zimbabwe 247237 0 5462
In [5]:
df.shape
Out[5]:
(161568, 5)

Data pre processing¶

In [6]:
df = df[df.Confirmed > 0]

This is the data from the date when 1st case of covid-19 was recorded

In [7]:
df.head()
Out[7]:
Date Country Confirmed Recovered Deaths
33 2020-02-24 Afghanistan 5 0 0
34 2020-02-25 Afghanistan 5 0 0
35 2020-02-26 Afghanistan 5 0 0
36 2020-02-27 Afghanistan 5 0 0
37 2020-02-28 Afghanistan 5 0 0
In [8]:
df.shape
Out[8]:
(148455, 5)
In [9]:
df[df.Country == 'China']
Out[9]:
Date Country Confirmed Recovered Deaths
30192 2020-01-22 China 548 28 17
30193 2020-01-23 China 643 30 18
30194 2020-01-24 China 920 36 26
30195 2020-01-25 China 1406 39 42
30196 2020-01-26 China 2075 49 56
... ... ... ... ... ...
31003 2022-04-12 China 1655477 0 13524
31004 2022-04-13 China 1681437 0 13586
31005 2022-04-14 China 1705231 0 13640
31006 2022-04-15 China 1759128 0 13707
31007 2022-04-16 China 1760211 0 13748

816 rows × 5 columns

Covid-19 data of China

Global spread of Covid-19¶

In [10]:
fig = px.choropleth(df, locations = 'Country', locationmode = 'country names', color = 'Confirmed'
                    , animation_frame = 'Date')
fig.update_layout(title_text = "Global spread of Covid-19")
fig.show()

Global death from covid-19¶

In [11]:
fig = px.choropleth(df, locations = 'Country', locationmode = 'country names', color = 'Deaths'
                    , animation_frame = 'Date')
fig.update_layout(title_text = "Global death from Covid-19")
fig.show()

Covid-19 tramission in India¶

In [12]:
df_india = df[df.Country == 'India']
df_india.head()
Out[12]:
Date Country Confirmed Recovered Deaths
65288 2020-01-30 India 1 0 0
65289 2020-01-31 India 1 0 0
65290 2020-02-01 India 1 0 0
65291 2020-02-02 India 2 0 0
65292 2020-02-03 India 3 0 0
In [13]:
df_india = df_india[['Date','Confirmed']]
In [14]:
df_india.head()
Out[14]:
Date Confirmed
65288 2020-01-30 1
65289 2020-01-31 1
65290 2020-02-01 1
65291 2020-02-02 2
65292 2020-02-03 3

calculating the first derivation of confirmed column

In [15]:
df_india['Infection Rate'] = df_india['Confirmed'].diff()
In [16]:
df_india.head(10)
Out[16]:
Date Confirmed Infection Rate
65288 2020-01-30 1 NaN
65289 2020-01-31 1 0.0
65290 2020-02-01 1 0.0
65291 2020-02-02 2 1.0
65292 2020-02-03 3 1.0
65293 2020-02-04 3 0.0
65294 2020-02-05 3 0.0
65295 2020-02-06 3 0.0
65296 2020-02-07 3 0.0
65297 2020-02-08 3 0.0

According to the data on 30st Jan 2020 there was first covid confirmed case in India.

In [17]:
px.line(df_india, x = 'Date', y = ['Confirmed','Infection Rate'])
In [18]:
df_india['Infection Rate'].max()
Out[18]:
414188.0

India witness 414188 people were infected in single day

Covid-19 tramission in China¶

In [19]:
df_china = df[df.Country == 'China']
df_china.head()
Out[19]:
Date Country Confirmed Recovered Deaths
30192 2020-01-22 China 548 28 17
30193 2020-01-23 China 643 30 18
30194 2020-01-24 China 920 36 26
30195 2020-01-25 China 1406 39 42
30196 2020-01-26 China 2075 49 56
In [20]:
df_china = df_china[['Date','Confirmed']]
In [21]:
df_china.head()
Out[21]:
Date Confirmed
30192 2020-01-22 548
30193 2020-01-23 643
30194 2020-01-24 920
30195 2020-01-25 1406
30196 2020-01-26 2075
In [22]:
df_china['Infection Rate'] = df_china['Confirmed'].diff()
In [23]:
df_china.head(10)
Out[23]:
Date Confirmed Infection Rate
30192 2020-01-22 548 NaN
30193 2020-01-23 643 95.0
30194 2020-01-24 920 277.0
30195 2020-01-25 1406 486.0
30196 2020-01-26 2075 669.0
30197 2020-01-27 2877 802.0
30198 2020-01-28 5509 2632.0
30199 2020-01-29 6087 578.0
30200 2020-01-30 8141 2054.0
30201 2020-01-31 9802 1661.0

According to the data on 22 Jan 2022 China registered 1st Covid case

In [24]:
px.line(df_china, x = 'Date', y = ['Confirmed','Infection Rate'])
In [25]:
df_china['Infection Rate'].max()
Out[25]:
77402.0

China witness 77402 people were infected in single day

Creating new dataframe of maximun infection rate(MIF)¶

In [26]:
countries = list(df['Country'].unique())
max_infection_rate = []
for c in countries :
    MIR = df[df.Country == c].Confirmed.diff().max()
    max_infection_rate.append(MIR)
In [27]:
df_MIR = pd.DataFrame()
df_MIR['Country'] = countries
df_MIR['Max Infection Rate'] = max_infection_rate
df_MIR.head(10)
Out[27]:
Country Max Infection Rate
0 Afghanistan 3243.0
1 Albania 4789.0
2 Algeria 2521.0
3 Andorra 2313.0
4 Angola 5035.0
5 Antarctica 0.0
6 Antigua and Barbuda 468.0
7 Argentina 139853.0
8 Armenia 4388.0
9 Australia 175271.0
In [28]:
px.bar(df_MIR, x = 'Country', y = 'Max Infection Rate', color = 'Country', title = 'Global maximum infection rate')

US is the country with highest Infection Rate followed by UK and Turkey

In [29]:
px.bar(df_MIR, x = 'Country', y = 'Max Infection Rate', color = 'Country', title = 'Global maximum infection rate expanded', log_y = True )

How national Lockdown impact on Covid-19 transmission in India¶

On March 24, 2020, Prime Minister Narendra Modi called for a complete lockdown of the entire nation for 21 days in an effort to contain the COVID-19 pandemic. In a televised address to the nation, the PM said that even those nations with the best medical facilities could not contain the virus and that social distancing is the only option to mitigate it. He said this decision was taken from the experiences of health sector experts and experiences of other countries and that 21 days is essential to break the chain of infection.

Source https://economictimes.indiatimes.com/defaultinterstitial.cms

In [30]:
india_lockdown_start_date = '2020-03-24'
india_lockdown_a_month_later = '2020-04-24'
india_lockdown_six_months_later = '2020-09-24'
In [31]:
df.head()
Out[31]:
Date Country Confirmed Recovered Deaths
33 2020-02-24 Afghanistan 5 0 0
34 2020-02-25 Afghanistan 5 0 0
35 2020-02-26 Afghanistan 5 0 0
36 2020-02-27 Afghanistan 5 0 0
37 2020-02-28 Afghanistan 5 0 0
In [32]:
df_india = df[df.Country == 'India']
In [33]:
df_india.head()
Out[33]:
Date Country Confirmed Recovered Deaths
65288 2020-01-30 India 1 0 0
65289 2020-01-31 India 1 0 0
65290 2020-02-01 India 1 0 0
65291 2020-02-02 India 2 0 0
65292 2020-02-03 India 3 0 0
In [34]:
df_india['Infection Rate'] = df_india.Confirmed.diff()
df_india.head()

C:\Users\bhatt\AppData\Local\Temp\ipykernel_52920\528034351.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Out[34]:
Date Country Confirmed Recovered Deaths Infection Rate
65288 2020-01-30 India 1 0 0 NaN
65289 2020-01-31 India 1 0 0 0.0
65290 2020-02-01 India 1 0 0 0.0
65291 2020-02-02 India 2 0 0 1.0
65292 2020-02-03 India 3 0 0 1.0
In [35]:
fig = px.line(df_india, x= 'Date', y = 'Infection Rate', title = 'Before and after lockdown in India ')
fig.add_shape(
    dict(
    type = 'line',
    x0 = india_lockdown_start_date,
    y0 = 0,
    x1 = india_lockdown_start_date,
    y1 = df_india['Infection Rate'].max(),
    line = dict(color = 'red', width = 2)
    )
)
fig.add_annotation(
dict(
    x = india_lockdown_start_date,
    y = df_india['Infection Rate'].max(),
    text = 'starting date of the lockdown'
    )

)
fig.add_shape(
    dict(
    type = 'line',
    x0 = india_lockdown_a_month_later,
    y0 = 0,
    x1 = india_lockdown_a_month_later,
    y1 = df_india['Infection Rate'].max(),
    line = dict(color = 'orange', width = 2))
)
fig.add_annotation(
dict(
    x = india_lockdown_a_month_later,
    y = 0,
    text = '1 month of the lockdown'
    )
)

Impact of lockdown after 6 months¶

In [36]:
fig = px.line(df_india, x= 'Date', y = 'Infection Rate', title = 'Before and after lockdown in India ')
fig.add_shape(
    dict(
    type = 'line',
    x0 = india_lockdown_start_date,
    y0 = 0,
    x1 = india_lockdown_start_date,
    y1 = df_india['Infection Rate'].max(),
    line = dict(color = 'red', width = 2)
    )
)
fig.add_annotation(
dict(
    x = india_lockdown_start_date,
    y = df_india['Infection Rate'].max(),
    text = 'starting date of the lockdown'
    )

)
fig.add_shape(
    dict(
    type = 'line',
    x0 = india_lockdown_six_months_later,
    y0 = 0,
    x1 = india_lockdown_six_months_later,
    y1 = df_india['Infection Rate'].max(),
    line = dict(color = 'orange', width = 2))
)
fig.add_annotation(
dict(
    x = india_lockdown_six_months_later,
    y = 0,
    text = '6 months of the lockdown'
    )
)

How lockdown impacts Covod-19 death rate in India¶

In [37]:
df_india.head()
Out[37]:
Date Country Confirmed Recovered Deaths Infection Rate
65288 2020-01-30 India 1 0 0 NaN
65289 2020-01-31 India 1 0 0 0.0
65290 2020-02-01 India 1 0 0 0.0
65291 2020-02-02 India 2 0 0 1.0
65292 2020-02-03 India 3 0 0 1.0
In [38]:
df_india['Death Rate'] = df_india.Deaths.diff()

C:\Users\bhatt\AppData\Local\Temp\ipykernel_52920\2009258026.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
In [39]:
df_india.head()
Out[39]:
Date Country Confirmed Recovered Deaths Infection Rate Death Rate
65288 2020-01-30 India 1 0 0 NaN NaN
65289 2020-01-31 India 1 0 0 0.0 0.0
65290 2020-02-01 India 1 0 0 0.0 0.0
65291 2020-02-02 India 2 0 0 1.0 0.0
65292 2020-02-03 India 3 0 0 1.0 0.0
In [40]:
fig = px.line(df_india, x = 'Date', y = ['Infection Rate', 'Death Rate'])
fig.show()
In [41]:
df_india['Infection Rate'] = df_india['Infection Rate']/df_india['Infection Rate'].max()
df_india['Death Rate'] = df_india['Death Rate']/df_india['Death Rate'].max()

C:\Users\bhatt\AppData\Local\Temp\ipykernel_52920\2459862756.py:1: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

C:\Users\bhatt\AppData\Local\Temp\ipykernel_52920\2459862756.py:2: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

Impact of lockdown after 1 month¶

In [47]:
fig = px.line(df_india, x = 'Date', y = ['Infection Rate', 'Death Rate'])
fig.add_shape(
    dict(
    type = 'line',
    x0 = india_lockdown_start_date,
    y0 = 0,
    x1 = india_lockdown_start_date,
    y1 = df_india['Infection Rate'].max(),
    line = dict(color = 'red', width = 2)
    )
)
fig.add_annotation(
dict(
    x = india_lockdown_start_date,
    y = df_india['Infection Rate'].max(),
    text = 'starting date of the lockdown'
    )

)
fig.add_shape(
    dict(
    type = 'line',
    x0 = india_lockdown_a_month_later,
    y0 = 0,
    x1 = india_lockdown_a_month_later,
    y1 = df_india['Infection Rate'].max(),
    line = dict(color = 'orange', width = 2))
)
fig.add_annotation(
dict(
    x = india_lockdown_a_month_later,
    y = 0,
    text = '1 month of the lockdown'
    )
)

Impact of lockdown after 6 months¶

In [46]:
fig = px.line(df_india, x = 'Date', y = ['Infection Rate', 'Death Rate'])
fig.add_shape(
    dict(
    type = 'line',
    x0 = india_lockdown_start_date,
    y0 = 0,
    x1 = india_lockdown_start_date,
    y1 = df_india['Infection Rate'].max(),
    line = dict(color = 'red', width = 2)
    )
)
fig.add_annotation(
dict(
    x = india_lockdown_start_date,
    y = df_india['Infection Rate'].max(),
    text = 'starting date of the lockdown'
    )

)
fig.add_shape(
    dict(
    type = 'line',
    x0 = india_lockdown_six_months_later,
    y0 = 0,
    x1 = india_lockdown_six_months_later,
    y1 = df_india['Infection Rate'].max(),
    line = dict(color = 'orange', width = 2))
)
fig.add_annotation(
dict(
    x = india_lockdown_six_months_later,
    y = 0,
    text = '6 months of the lockdown'
    )
)
In [ ]: